{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "# Data Science Basics in Python Series\n",
    "\n",
    "## Chapter III: Matplotlib for Univariate Data Visualization in Python \n",
    "\n",
    "### Michael Pyrcz, Associate Professor, The University of Texas at Austin \n",
    "\n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Data Visualization with MatPlotLib in Python for Engineers and Geoscientists \n",
    "\n",
    "This is a tutorial for / demonstration of **Univariate Data Visualization in Python**. In Python, a common tool for dealing with Data Visualization is the **Matplotlib Python package**\n",
    "\n",
    "* Initiated by John Hunter along with many contributors\n",
    "\n",
    "* Opensource project is a sponsored project of [NumFocus](https://numfocus.org/) \n",
    "\n",
    "This tutorial includes the methods and operations that would commonly be required for Engineers and Geoscientists working with Data Visualization for the purpose of:\n",
    "\n",
    "1. Data Checking and Cleaning\n",
    "2. Data Mining / Inferential Data Analysis\n",
    "3. Predictive Modeling\n",
    "\n",
    "for Data Analytics, Geostatistics and Machine Learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "##### Data Visualization\n",
    "\n",
    "Data visualization includes any graphical representation of the data. \n",
    "\n",
    "We will demonstate basic concepts with only:\n",
    "\n",
    "* univariate distributions, histograms\n",
    "\n",
    "We will start simple and add more complexity and customization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Project Goal\n",
    "\n",
    "Learn the basics for working with Univariate Data Visualization in Python to build practical spatial data analytics, geostatistics and machine learning workflows.\n",
    "\n",
    "* Focus on customization and not a survey of available plot times\n",
    "\n",
    "#### Caveats\n",
    "\n",
    "I included methods that I have found useful for building my geoscience and engineering workflows for subsurface modeling. I think they should be accessible to most geoscientists and engineers. Certainly, there are more advanced, more compact, more efficient methods to accomplish the same tasks. I tried to keep the methods simple. I appreciate feedback and I will use it to improve this tutorial periodically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Load and Configure the Required Libraries\n",
    "\n",
    "The following code loads the required libraries and sets a plotting default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import os                                       # operating system\n",
    "import numpy as np                              # arrays and matrix math\n",
    "import pandas as pd                             # tabular data\n",
    "import matplotlib.pyplot as plt                 # plotting\n",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator) # control of axes ticks\n",
    "plt.rc('axes', axisbelow=True)                  # set axes and grids in the background for all plots\n",
    "from matplotlib.patches import Rectangle        # drawing shapes on plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see below) data file in this directory.  When we are done with this tutorial we will write our new dataset back to this directory.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "#os.chdir(\"c:/PGE383\")                           # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Loading the Datasets\n",
    "\n",
    "Let's load a tabular dataset from another .csv file, [spatial_nonlinear_MV_facies_v1.csv](https://github.com/GeostatsGuy/GeoDataSets/blob/master/spatial_nonlinear_MV_facies_v1.csv)\n",
    "\n",
    "We cover loading data in these previous **Data Science Basics in Python** lectures, [tabular data](https://www.youtube.com/watch?v=rku5rZxS0AA) and [gridded data](https://www.youtube.com/watch?v=uCRkFwQqdJo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tabular data is a <class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Y</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>Facies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.006391</td>\n",
       "      <td>7.791849</td>\n",
       "      <td>332.802662</td>\n",
       "      <td>4114.121592</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>160.265186</td>\n",
       "      <td>16.708829</td>\n",
       "      <td>505.072608</td>\n",
       "      <td>3820.596087</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>775.834642</td>\n",
       "      <td>12.430224</td>\n",
       "      <td>404.367985</td>\n",
       "      <td>4180.556194</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Y   Porosity        Perm           AI  Facies\n",
       "0   10.006391   7.791849  332.802662  4114.121592       1\n",
       "1  160.265186  16.708829  505.072608  3820.596087       1\n",
       "2  775.834642  12.430224  404.367985  4180.556194       1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#table = pd.read_csv('spatial_nonlinear_MV_facies_v1.csv') # load the tabular dataset\n",
    "table = pd.read_csv('https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/spatial_nonlinear_MV_facies_v1.csv') # load from Dr. Pyrcz's github account\n",
    "\n",
    "table = table.iloc[:,1:]                        # remove the first feature (column)\n",
    "print('The tabular data is a ' + str(type(table)))     \n",
    "table.head(n=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract the Feature from the Table\n",
    "\n",
    "I do this for concise and readable code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The por is a <class 'numpy.ndarray'> of shape (457,).\n"
     ]
    }
   ],
   "source": [
    "por = table['Porosity'].values                  # extract porosity feature a a 1D ndarray\n",
    "print('The por is a ' + str(type(por)) + ' of shape ' + str(por.shape) + '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Histograms\n",
    "\n",
    "Here's the basic histogram plot.\n",
    "\n",
    "* Quite a plain plot\n",
    "* Axes unlabeled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x=table['Porosity'].values)            # basic histogram\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Design the Plot Space\n",
    "\n",
    "Let's improve the plot by considering and designing the plot space.\n",
    "    \n",
    "* Label the axes (.xlabel(),.ylabel()), always the 'right' answer!\n",
    "* Add a grid (.grid()) to improve our ability to perform 'ocular inspection'\n",
    "* We explicity control the plot size, start considering readability\n",
    "* Consider color (color = string) to separate elements, i.e. foreground and background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x=por,color='red')                     # histogram\n",
    "plt.xlabel('Porosity (%)'); plt.ylabel('Frequency'); plt.title('Porosity Histogram') # axes labels\n",
    "plt.grid()                                      # add grid\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compose the Elements\n",
    "\n",
    "Let's think more about how we can combine all the elements to improve clarity\n",
    "\n",
    "* Outline the histogram bars (edgecolor = string) to separate the binning of the data\n",
    "* Use transparency (alpha < 1.0) to further improve 'ocular inspection' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(alpha = 0.2,edgecolor='black',x=por,color='red'); plt.xlabel('Porosity (%)'); plt.ylabel('Frequency'); plt.title('Porosity Histogram') # axes labels\n",
    "plt.grid()                                      # add grid\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Improve the Consistency Between Elements\n",
    "\n",
    "Let's improve the consistency of the plot elements.\n",
    "\n",
    "* Specify the axes' extents (.xlim(),.ylim()) and align yaxes increments with integer frequency\n",
    "* Only show grid on y and add a minor grid and ticks for readibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bins=np.linspace(0,30,20),alpha = 0.2,edgecolor='black',x=por,color='red'); plt.xlabel('Porosity (%)'); plt.ylabel('Frequency'); plt.title('Porosity Histogram') # axes labels\n",
    "plt.xlim(0,30); plt.ylim(0,80)                      # constrain the axes' extents                           \n",
    "plt.gca().yaxis.grid(True, which='major',linewidth = 1.0); plt.gca().yaxis.grid(True, which='minor',linewidth = 0.2) # add y grids\n",
    "plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4)\n",
    "plt.gca().xaxis.set_minor_locator(AutoMinorLocator()); plt.gca().yaxis.set_minor_locator(AutoMinorLocator()) # turn on minor ticks\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histogram Ultimate Control\n",
    "\n",
    "\n",
    "Let's further improve the consistency between our plot elements and add hierarchy to the labels\n",
    "\n",
    "* Specify the histogram bins (bins = list), grid and ticks to align with histogram bins \n",
    "* Adjust the font sizes (fontsize = float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bins=np.linspace(0,30,31),alpha = 0.4,edgecolor='black',x=por,color='red'); plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4); plt.gca().yaxis.grid(True, which='major',linewidth = 1.0); plt.gca().yaxis.grid(True, which='minor',linewidth = 0.2) # add y grid\n",
    "plt.xlabel('Porosity (%)',fontsize=15); plt.ylabel('Frequency',fontsize=15); plt.title('Porosity Histogram',fontsize=20) # axes labels\n",
    "plt.xlim(0,30); plt.ylim(0,45)                      # constrain the axes' extents                           \n",
    "plt.gca().xaxis.set_major_locator(MultipleLocator(1)) # set the major ticks aligned with the bins\n",
    "plt.gca().yaxis.set_minor_locator(MultipleLocator(1)); plt.gca().yaxis.set_major_locator(MultipleLocator(5)) # set the minor ticks for integer frequency\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make a Custom Plot Formatting Function\n",
    "\n",
    "This is a lot of code, let's make a custom function to format our histogram\n",
    "\n",
    "* We could make our function more flexible with the addition of function arguments\n",
    "\n",
    "* This is helpful for concise workflows, especially if similar plots are reused\n",
    "\n",
    "* We will not discuss the definition and use of **styles**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_hist():                                  # function declaration\n",
    "    plt.xlabel('Porosity (%)',fontsize=15); plt.ylabel('Frequency',fontsize=15); plt.title('Porosity Histogram',fontsize=20) # axes labels\n",
    "    plt.xlim(0,30); plt.ylim(0,45)                      # constrain the axes' extents                           \n",
    "    plt.gca().yaxis.grid(True, which='major',linewidth = 1.0) # add y major grid\n",
    "    plt.gca().yaxis.grid(True, which='minor',linewidth = 0.2) # add y minor grid\n",
    "    plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4)\n",
    "    plt.gca().xaxis.set_major_locator(MultipleLocator(1)) # set the major ticks aligned with the bins\n",
    "    plt.gca().yaxis.set_minor_locator(MultipleLocator(1)) # set the minor ticks for integer frequency\n",
    "    plt.gca().yaxis.set_major_locator(MultipleLocator(5)) # set the minor ticks for integer frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histogram with Custom Formatting Function\n",
    "\n",
    "Let's demonstrate the application of the format function\n",
    "\n",
    "* Same result with much less code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bins=np.linspace(0,30,31),alpha = 0.4,edgecolor='black',x=por,color='red')\n",
    "format_hist()                                       # insert the custom plot formatting                                    \n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add a Custom Legend\n",
    "\n",
    "We may want to communicate key statistics with a custom legend by adding shapes and annotation to our plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bins=np.linspace(0,30,31),alpha = 0.4,edgecolor='black',x=por,color='red')\n",
    "format_hist()                                        # insert the custom plot formatting \n",
    "plt.gca().add_patch(Rectangle((23.5,33.5),4.5,9,facecolor='white',edgecolor='black',linewidth=0.5))\n",
    "plt.text(24,40.5,'Mean ' + str(round(np.average(por),1))); plt.text(24,38.5,'P90    ' + str(round(np.percentile(por,90),1)))\n",
    "plt.text(24,36.5,'P10    ' + str(round(np.percentile(por,10),1))); plt.text(24,34.5,'n        ' + str(por.shape[0]))\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Highlight Data Features\n",
    "\n",
    "Let's highlight the summary statistics on the plot with lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bins=np.linspace(0,30,31),alpha = 0.4,edgecolor='black',x=por,color='red')\n",
    "format_hist()                                        # insert the custom plot formatting \n",
    "plt.gca().add_patch(Rectangle((23.5,33.5),4.5,9,facecolor='white',edgecolor='black',linewidth=0.5)); plt.text(24,40.5,'Mean ' + str(round(np.average(por),1))); plt.text(24,38.5,'P90    ' + str(round(np.percentile(por,90),1))); plt.text(24,36.5,'P10    ' + str(round(np.percentile(por,10),1))); plt.text(24,34.5,'n        ' + str(por.shape[0]))\n",
    "p10 = np.percentile(por,10); avg = np.average(por); p90 = np.percentile(por,90)\n",
    "plt.plot([p10,p10],[0.0,45],color = 'black',linestyle='dashed')\n",
    "plt.plot([avg,avg],[0.0,45],color = 'black')\n",
    "plt.plot([p90,p90],[0.0,45],color = 'black',linestyle='dashed')\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From Start to End\n",
    "\n",
    "Let's compare our original and final plots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "plt.hist(por)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.hist(bins=np.linspace(0,30,31),alpha = 0.4,edgecolor='black',x=por,color='red')\n",
    "format_hist()                                        # insert the custom plot formatting \n",
    "plt.gca().add_patch(Rectangle((23.7,33.5),4.5,9,facecolor='white',edgecolor='black',linewidth=0.5)); plt.text(24,40.5,'Mean ' + str(round(np.average(por),1))); plt.text(24,38.5,'P90    ' + str(round(np.percentile(por,90),1))); plt.text(24,36.5,'P10    ' + str(round(np.percentile(por,10),1))); plt.text(24,34.5,'n        ' + str(por.shape[0]))\n",
    "p10 = np.percentile(por,10); avg = np.average(por); p90 = np.percentile(por,90)\n",
    "plt.plot([p10,p10],[0.0,45],color = 'black',linestyle='dashed')\n",
    "plt.plot([avg,avg],[0.0,45],color = 'black')\n",
    "plt.plot([p90,p90],[0.0,45],color = 'black',linestyle='dashed')\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=2.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multiple Plots\n",
    "\n",
    "Now let's separate the samples by facies. For more information see the [tabular data](https://www.youtube.com/watch?v=rku5rZxS0AA) lecture.\n",
    "\n",
    "* Facies 0 - shalestone\n",
    "* Facies 1 - sandstone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 360 sandstone samples.\n",
      "There are 97 shalestone samples.\n"
     ]
    }
   ],
   "source": [
    "sand = table[table['Facies'] == 1]['Porosity'].values # extract sandstone samples\n",
    "shale = table[table['Facies'] == 0]['Porosity'].values # extract shalestone samples\n",
    "print('There are ' + str(len(sand)) + ' sandstone samples.')\n",
    "print('There are ' + str(len(shale)) + ' shalestone samples.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combine Multiple Plots with Subplots\n",
    "\n",
    "We can combine plots with subplots.\n",
    "\n",
    "* the subplot specification is [$n_{rows}$][$n_{columns}$][current index], current index is from 1 to $n_{rows} \\times n_{columns}$\n",
    "* index is from top left accross columns and then down (like reading a page)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)                                    # 1 row, 2 columns, 1st plot\n",
    "plt.hist(x=sand,color='yellow',alpha=0.5,edgecolor='black',bins=np.linspace(0,30,31),label='Sand') # improved histogram\n",
    "plt.legend(); format_hist()                         # add a legend and formatting\n",
    "plt.subplot(122)                                    # 1 row, 2 columns, 2nd plot\n",
    "plt.hist(x=shale,color='brown',alpha=0.5,edgecolor='black',bins=np.linspace(0,30,31),label='Shale') # improved histogram\n",
    "plt.legend(); format_hist()                         # add a legend and formatting\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=2.0,wspace=0.2,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combine Multiple Plots with in Same Plot Space\n",
    "\n",
    "We can combine plots in the same plot space. We do this by adding plots in sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x=sand,color='yellow',alpha=0.5,edgecolor='black',bins=np.linspace(0,30,31),label='Sand') # improved histogram\n",
    "plt.hist(x=shale,color='brown',alpha=0.5,edgecolor='black',bins=np.linspace(0,30,31),label='Shale') # improved histogram\n",
    "plt.legend()                                        # add a legend\n",
    "format_hist()                                       # custom formatting\n",
    "                                   \n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Specifying Plot Order \n",
    "\n",
    "We can use the zorder argument to specify the plotting order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "plt.hist(zorder=1,x=sand,color='yellow',alpha=1.0,edgecolor='black',bins=np.linspace(0,30,31),label='Sand') # improved histogram\n",
    "plt.hist(zorder=2,x=shale,color='brown',alpha=1.0,edgecolor='black',bins=np.linspace(0,30,31),label='Shale') # improved histogram\n",
    "plt.legend()                                        # add a legend\n",
    "format_hist()                                       # custom formatting\n",
    " \n",
    "plt.subplot(122)\n",
    "plt.hist(zorder=2,x=sand,color='yellow',alpha=1.0,edgecolor='black',bins=np.linspace(0,30,31),label='Sand') # improved histogram\n",
    "plt.hist(zorder=1,x=shale,color='brown',alpha=1.0,edgecolor='black',bins=np.linspace(0,30,31),label='Shale') # improved histogram\n",
    "plt.legend()                                        # add a legend\n",
    "format_hist()                                       # custom formatting\n",
    "    \n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=2.0,top=1.1,wspace=0.2); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding PDF to the Histogram\n",
    "\n",
    "We can combine multiple types of plots. Here we add the PDFs estimated with kernel density estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns                               # plotting\n",
    "plt.hist(zorder=1,x=sand,color='yellow',alpha=0.5,edgecolor='black',bins=np.linspace(0,30,31),label='Sand',density=True) # improved histogram\n",
    "plt.hist(zorder=2,x=shale,color='brown',alpha=0.5,edgecolor='black',bins=np.linspace(0,30,31),label='Shale',density=True) # improved histogram\n",
    "sns.kdeplot(x=sand,color = 'orange',alpha = 0.8,levels = 1)\n",
    "sns.kdeplot(x=shale,color = 'black',alpha = 0.8,levels = 1)\n",
    "plt.legend()                                        # add a legend\n",
    "plt.xlabel('Porosity (%)'); plt.ylabel('Density / Probability'); plt.title('Normalized Histograms and PDFs')\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1,wspace=0.2); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting a Cumulative Distribution Function\n",
    "\n",
    "It is quite easy to switch from histogram to CDF.\n",
    "\n",
    "* We set the cumulative argument to True.\n",
    "* We switch the histtype argument to step or stepfilled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(cumulative=True,histtype='stepfilled',x=shale,color='brown',alpha=1.0,edgecolor='black',bins=np.linspace(0,30,400),label='Shale',density=True) # improved histogram\n",
    "plt.hist(cumulative=True,histtype='stepfilled',x=sand,color='yellow',alpha=1.0,edgecolor='black',bins=np.linspace(0,30,400),label='Sand',density=True) # improved histogram\n",
    "plt.legend(loc='upper left'); plt.xlim(0,30); plt.ylim(0,1) # add a legend\n",
    "plt.xlabel('Porosity (%)'); plt.ylabel('Cumulative Probabilities'); plt.title('Cumulative Distribution Function')\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "I hope this we helpful,\n",
    "\n",
    "Michael\n",
    "\n",
    "#### More About The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, The University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin"
   ]
  }
 ],
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